A robust, parallelizable, <i>O</i>(<i>m</i>), a <i>posteriori</i> recursive least squares algorithm for efficient adaptive filtering

作     者：Papaodysseus, C 

作者机构：Natl Tech Univ Athens Dept Elect & Comp Engn Div Comp Engn Athens Greece 

出 版 物：《IEEE TRANSACTIONS ON SIGNAL PROCESSING》 (IEEE Trans Signal Process)

年 卷 期：1999年第47卷第9期

页      面：2552-2558页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

主　　题：adaptive algorithms adaptive filtering finite precision error finite memory algorithms finite window filtering Kalman-type algorithms quantization error RLS algorithms stabilized algorithms 

摘      要：This correspondence presents a new recursive least squares (RLS) adaptive algorithm. The proposed computational scheme uses a finite window by means of a lemma for the system matrix inversion that is, for the first time, stated and proven here. The new algorithm has excellent tracking capabilities. Moreover, its particular structure allows for stabilization by means of a quite simple method. Its stabilized version performs very well not only for a white noise input but also for nonstationary inputs as well. It is shown to follow music, speech, environmental noise, etc, with particularly good tracking properties. The new algorithm can be parallelized via a simple technique. Its parallel form is very fast when implemented with four processors.